Measuring cardiomyocyte cellular characteristics in cardiac hypertrophy using diffusion-weighted MRI.

PURPOSE: This paper presents a hierarchical modeling approach for estimating cardiomyocyte major and minor diameters and intracellular volume fraction (ICV) using diffusion-weighted MRI (DWI) data in ex vivo mouse hearts. METHODS: DWI data were acquired on two healthy controls and two hearts 3 weeks post transverse aortic constriction (TAC) using a bespoke diffusion scheme with multiple diffusion times ( Δ $$ \Delta $$ ), q-shells and diffusion encoding directions. Firstly, a bi-exponential tensor model was fitted separately at each diffusion time to disentangle the dependence on diffusion times from diffusion weightings, that is, b-values. The slow-diffusing component was attributed to the restricted diffusion inside cardiomyocytes. ICV was then extrapolated at Δ = 0 $$ \Delta =0 $$ using linear regression. Secondly, given the secondary and the tertiary diffusion eigenvalue measurements for the slow-diffusing component obtained at different diffusion times, major and minor diameters were estimated assuming a cylinder model with an elliptical cross-section (ECS). High-resolution three-dimensional synchrotron X-ray imaging (SRI) data from the same specimen was utilized to evaluate the biophysical parameters. RESULTS: Estimated parameters using DWI data were (control 1/control 2 vs. TAC 1/TAC 2): major diameter-17.4 µ $$ \mu $$ m/18.0 µ $$ \mu $$ m versus 19.2 µ $$ \mu $$ m/19.0 µ $$ \mu $$ m; minor diameter-10.2 µ $$ \mu $$ m/9.4 µ $$ \mu $$ m versus 12.8 µ $$ \mu $$ m/13.4 µ $$ \mu $$ m; and ICV-62%/62% versus 68%/47%. These findings were consistent with SRI measurements. CONCLUSION: The proposed method allowed for accurate estimation of biophysical parameters suggesting cardiomyocyte diameters as sensitive biomarkers of hypertrophy in the heart.

Origami: Single-cell 3D shape dynamics oriented along the apico-basal axis of folding epithelia from fluorescence microscopy data.

A common feature of morphogenesis is the formation of three-dimensional structures from the folding of two-dimensional epithelial sheets, aided by cell shape changes at the cellular-level. Changes in cell shape must be studied in the context of cell-polarised biomechanical processes within the epithelial sheet. In epithelia with highly curved surfaces, finding single-cell alignment along a biological axis can be difficult to automate in silico. We present 'Origami', a MATLAB-based image analysis pipeline to compute direction-variant cell shape features along the epithelial apico-basal axis. Our automated method accurately computed direction vectors denoting the apico-basal axis in regions with opposing curvature in synthetic epithelia and fluorescence images of zebrafish embryos. As proof of concept, we identified different cell shape signatures in the developing zebrafish inner ear, where the epithelium deforms in opposite orientations to form different structures. Origami is designed to be user-friendly and is generally applicable to fluorescence images of curved epithelia.

Utility of pre-treatment FDG PET/CT-derived machine learning models for outcome prediction in classical Hodgkin lymphoma.

OBJECTIVES: Relapse occurs in ~20% of patients with classical Hodgkin lymphoma (cHL) despite treatment adaption based on 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography response. The objective was to evaluate pre-treatment FDG PET/CT-derived machine learning (ML) models for predicting outcome in patients with cHL. METHODS: All cHL patients undergoing pre-treatment PET/CT at our institution between 2008 and 2018 were retrospectively identified. A 1.5 × mean liver standardised uptake value (SUV) and a fixed 4.0 SUV threshold were used to segment PET/CT data. Feature extraction was performed using PyRadiomics with ComBat harmonisation. Training (80%) and test (20%) cohorts stratified around 2-year event-free survival (EFS), age, sex, ethnicity and disease stage were defined. Seven ML models were trained and hyperparameters tuned using stratified 5-fold cross-validation. Area under the curve (AUC) from receiver operator characteristic analysis was used to assess performance. RESULTS: A total of 289 patients (153 males), median age 36 (range 16-88 years), were included. There was no significant difference between training (n = 231) and test cohorts (n = 58) (p value > 0.05). A ridge regression model using a 1.5 × mean liver SUV segmentation had the highest performance, with mean training, validation and test AUCs of 0.82 ± 0.002, 0.79 ± 0.01 and 0.81 ± 0.12. However, there was no significant difference between a logistic model derived from metabolic tumour volume and clinical features or the highest performing radiomic model. CONCLUSIONS: Outcome prediction using pre-treatment FDG PET/CT-derived ML models is feasible in cHL patients. Further work is needed to determine optimum predictive thresholds for clinical use. KEY POINTS: • A fixed threshold segmentation method led to more robust radiomic features. • A radiomic-based model for predicting 2-year event-free survival in classical Hodgkin lymphoma patients is feasible. • A predictive model based on ridge regression was the best performing model on our dataset.

Diffusion MRI for Assessment of Bone Quality; A Review of Findings in Healthy Aging and Osteoporosis.

Diffusion MRI (dMRI) is a growing imaging technique with the potential to provide biomarkers of tissue variation, such as cellular density, tissue anisotropy, and microvascular perfusion. However, the role of dMRI in characterizing different aspects of bone quality, especially in aging and osteoporosis, has not yet been fully established, particularly in clinical applications. The reason lies in the complications accompanied with implementation of dMRI in assessment of human bone structure, in terms of acquisition and quantification. Bone is a composite tissue comprising different elements, each contributing to the overall quality and functional competence of bone. As diffusion is a critical biophysical process in biological tissues, early changes of tissue microstructure and function can affect diffusive properties of the tissue. While there are multiple MRI methods to detect variations of individual properties of bone quality due to aging and osteoporosis, dMRI has potential to serve as a superior method for characterizing different aspects of bone quality within the same framework but with higher sensitivity to early alterations. This is mainly because several properties of the tissue including directionality and anisotropy of trabecular bone and cell density can be collected using only dMRI. In this review article, we first describe components of human bone that can be potentially detected by their diffusivity properties and contribute to variations in bone quality during aging and osteoporosis. Then we discuss considerations and challenges of dMRI in bone imaging, current status, and suggestions for development of dMRI in research studies and clinics to segregate different contributing components of bone quality in an integrated acquisition. Level of Evidence: 5 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:975-992.

Population-based Bayesian regularization for microstructural diffusion MRI with NODDIDA.

PURPOSE: Information on the brain microstructure can be probed by Diffusion Magnetic Resonance Imaging (dMRI). Neurite Orientation Dispersion and Density Imaging with Diffusivities Assessment (NODDIDA) is one of the simplest microstructural model proposed. However, the estimation of the NODDIDA parameters from clinically plausible dMRI acquisition is ill-posed, and different parameter sets can describe the same measurements equally well. A few approaches to resolve this problem focused on developing better optimization strategies for this non-convex optimization. However, this fundamentally does not resolve ill-posedness. This article introduces a Bayesian estimation framework, which is regularized through knowledge from an extensive dMRI measurement set on a population of healthy adults (henceforth population-based prior). METHODS: We reformulate the problem as a Bayesian maximum a posteriori estimation, which includes as a special case previous approach using non-informative uniform priors. A population-based prior is estimated from 35 subjects of the MGH Adult Diffusion data (Human Connectome Project), acquired with an extensive acquisition protocol including high b-values. The accuracy and robustness of different approaches with and without the population-based prior is tested on subsets of the MGH dataset, and an independent dataset from a clinically comparable scanner, with only clinically plausible dMRI measurements. RESULTS: The population-based prior produced substantially more accurate and robust parameter estimates, compared to the conventional uniform priors, for clinically feasible protocols, without introducing any evident bias. CONCLUSIONS: The use of the proposed Bayesian population-based prior can lead to clinically feasible and robust estimation of NODDIDA parameters without changing the acquisition protocol.

Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding.

PURPOSE: Biophysical tissue models are increasingly used in the interpretation of diffusion MRI (dMRI) data, with the potential to provide specific biomarkers of brain microstructural changes. However, it has been shown recently that, in the general Standard Model, parameter estimation from dMRI data is ill-conditioned even when very high b-values are applied. We analyze this issue for the Neurite Orientation Dispersion and Density Imaging with Diffusivity Assessment (NODDIDA) model and demonstrate that its extension from single diffusion encoding (SDE) to double diffusion encoding (DDE) resolves the ill-posedness for intermediate diffusion weightings, producing an increase in accuracy and precision of the parameter estimation. METHODS: We analyze theoretically the cumulant expansion up to fourth order in b of SDE and DDE signals. Additionally, we perform in silico experiments to compare SDE and DDE capabilities under similar noise conditions. RESULTS: We prove analytically that DDE provides invariant information non-accessible from SDE, which makes the NODDIDA parameter estimation injective. The in silico experiments show that DDE reduces the bias and mean square error of the estimation along the whole feasible region of 5D model parameter space. CONCLUSIONS: DDE adds additional information for estimating the model parameters, unexplored by SDE. We show, as an example, that this is sufficient to solve the previously reported degeneracies in the NODDIDA model parameter estimation.

Local volume fraction distributions of axons, astrocytes, and myelin in deep subcortical white matter.

This study aims to statistically describe histologically stained white matter brain sections to subsequently inform and validate diffusion MRI techniques. For the first time, we characterise volume fraction distributions of three of the main structures in deep subcortical white matter (axons, astrocytes, and myelinated axons) in a representative cohort of an ageing population for which well-characterized neuropathology data is available. We analysed a set of samples from 90 subjects of the Cognitive Function and Ageing Study (CFAS), stratified into three groups of 30 subjects each, in relation to the presence of age-associated deep subcortical lesions. This provides volume fraction distributions in different scenarios relevant to brain diffusion MRI in dementia. We also assess statistically significant differences found between these groups. In agreement with previous literature, our results indicate that white matter lesions are related with a decrease in the myelinated axons fraction and an increase in astrocytic fraction, while no statistically significant changes occur in axonal mean fraction. In addition, we introduced a framework to quantify volume fraction distributions from 2D immunohistochemistry images, which is validated against in silico simulations. Since a trade-off between precision and resolution emerged, we also performed an assessment of the optimal scale for computing such distributions.

Simultaneous magnetic resonance diffusion and pseudo-diffusion tensor imaging.

PURPOSE: An emerging topic in diffusion magnetic resonance is imaging blood microcirculation alongside water diffusion using the intravoxel incoherent motion (IVIM) model. Recently, a combined IVIM diffusion tensor imaging (IVIM-DTI) model was proposed, which accounts for both anisotropic pseudo-diffusion due to blood microcirculation and anisotropic diffusion due to tissue microstructures. In this article, we propose a robust IVIM-DTI approach for simultaneous diffusion and pseudo-diffusion tensor imaging. METHODS: Conventional IVIM estimation methods can be broadly divided into two-step (diffusion and pseudo-diffusion estimated separately) and one-step (diffusion and pseudo-diffusion estimated simultaneously) methods. Here, both methods were applied on the IVIM-DTI model. An improved one-step method based on damped Gauss-Newton algorithm and a Gaussian prior for the model parameters was also introduced. The sensitivities of these methods to different parameter initializations were tested with realistic in silico simulations and experimental in vivo data. RESULTS: The one-step damped Gauss-Newton method with a Gaussian prior was less sensitive to noise and the choice of initial parameters and delivered more accurate estimates of IVIM-DTI parameters compared to the other methods. CONCLUSION: One-step estimation using damped Gauss-Newton and a Gaussian prior is a robust method for simultaneous diffusion and pseudo-diffusion tensor imaging using IVIM-DTI model. Magn Reson Med 79:2367-2378, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI.

BACKGROUND: Targeted localized biopsies and treatments for diffuse gliomas rely on accurate identification of tissue subregions, for which current MRI techniques lack specificity. PURPOSE: To explore the complementary and competitive roles of a variety of conventional and quantitative MRI methods for distinguishing subregions of brain gliomas. STUDY TYPE: Prospective. POPULATION: Fifty-one tissue specimens were collected using image-guided localized biopsy surgery from 10 patients with newly diagnosed gliomas. FIELD STRENGTH/SEQUENCE: Conventional and quantitative MR images consisting of pre- and postcontrast T1 w, T2 w, T2 -FLAIR, T2 -relaxometry, DWI, DTI, IVIM, and DSC-MRI were acquired preoperatively at 3T. ASSESSMENT: Biopsy specimens were histopathologically attributed to glioma tissue subregion categories of active tumor (AT), infiltrative edema (IE), and normal tissue (NT) subregions. For each tissue sample, a feature vector comprising 15 MRI-based parameters was derived from preoperative images and assessed by a machine learning algorithm to determine the best multiparametric feature combination for characterizing the tissue subregions. STATISTICAL TESTS: For discrimination of AT, IE, and NT subregions, a one-way analysis of variance (ANOVA) test and for pairwise tissue subregion differentiation, Tukey honest significant difference, and Games-Howell tests were applied (P < 0.05). Cross-validated feature selection and classification methods were implemented for identification of accurate multiparametric MRI parameter combination. RESULTS: After exclusion of 17 tissue specimens, 34 samples (AT = 6, IE = 20, and NT = 8) were considered for analysis. Highest accuracies and statistically significant differences for discrimination of IE from NT and AT from NT were observed for diffusion-based parameters (AUCs >90%), and the perfusion-derived parameter as the most accurate feature in distinguishing IE from AT. A combination of "CBV, MD, T2 _ISO, FLAIR" parameters showed high diagnostic performance for identification of the three subregions (AUC ∼90%). DATA CONCLUSION: Integration of a few quantitative along with conventional MRI parameters may provide a potential multiparametric imaging biomarker for predicting the histopathologically proven glioma tissue subregions. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;48:938-950.

Evaluation of wave delivery methodology for brain MRE: Insights from computational simulations.

PURPOSE: MR elastography (MRE) of the brain is being explored as a biomarker of neurodegenerative disease such as dementia. However, MRE measures for healthy brain have varied widely. Differing wave delivery methodologies may have influenced this, hence finite element-based simulations were performed to explore this possibility. METHODS: The natural frequencies of a series of cranial models were calculated, and MRE-associated vibration was simulated for different wave delivery methods at varying frequency, using simple isotropic viscoelastic material models for the brain. Displacement fields and the corresponding brain constitutive properties estimated by standard inversion techniques were compared across delivery methods and frequencies. RESULTS: The delivery methods produced widely different MRE displacement fields and inversions. Furthermore, resonances at natural frequencies influenced the displacement patterns. Consequently, some delivery methods led to lower inversion errors than others, and the error on the storage modulus varied by up to 11% between methods. CONCLUSION: Wave delivery has a considerable impact on brain MRE reliability. Assuming small variations in brain biomechanics, as recently reported to accompany neurodegenerative disease (e.g., 7% for Alzheimer's disease), the effect of wave delivery is important. Hence, a consensus should be established on a consistent methodology to ensure diagnostic and prognostic consistency. Magn Reson Med 78:341-356, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Quantification of 1 H-MRS signals based on sparse metabolite profiles in the time-frequency domain.

MRS is an analytical approach used for both quantitative and qualitative analysis of human body metabolites. The accurate and robust quantification capability of proton MRS (1 H-MRS) enables the accurate estimation of living tissue metabolite concentrations. However, such methods can be efficiently employed for quantification of metabolite concentrations only if the overlapping nature of metabolites, existing static field inhomogeneity and low signal-to-noise ratio (SNR) are taken into consideration. Representation of 1 H-MRS signals in the time-frequency domain enables us to handle the baseline and noise better. This is possible because the MRS signal of each metabolite is sparsely represented, with only a few peaks, in the frequency domain, but still along with specific time-domain features such as distinct decay constant associated with T2 relaxation rate. The baseline, however, has a smooth behavior in the frequency domain. In this study, we proposed a quantification method using continuous wavelet transformation of 1 H-MRS signals in combination with sparse representation of features in the time-frequency domain. Estimation of the sparse representations of MR spectra is performed according to the dictionaries constructed from metabolite profiles. Results on simulated and phantom data show that the proposed method is able to quantify the concentration of metabolites in 1 H-MRS signals with high accuracy and robustness. This is achieved for both low SNR (5 dB) and low signal-to-baseline ratio (-5 dB) regimes.

Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments.

BACKGROUND: Understanding whether the cognitive profile of a patient indicates mild cognitive impairment (MCI) or performance levels within normality is often a clinical challenge. The use of resting-state functional magnetic resonance imaging (RS-fMRI) and machine learning may represent valid aids in clinical settings for the identification of MCI patients. METHODS: Machine-learning models were computed to test the classificatory accuracy of cognitive, volumetric [structural magnetic resonance imaging (sMRI)] and blood oxygen level dependent-connectivity (extracted from RS-fMRI) features, in single-modality and mixed classifiers. RESULTS: The best and most significant classifier was the RS-fMRI+Cognitive mixed classifier (94% accuracy), whereas the worst performing was the sMRI classifier (∼80%). The mixed global (sMRI+RS-fMRI+Cognitive) had a slightly lower accuracy (∼90%), although not statistically different from the mixed RS-fMRI+Cognitive classifier. The most important cognitive features were indices of declarative memory and semantic processing. The crucial volumetric feature was the hippocampus. The RS-fMRI features selected by the algorithms were heavily based on the connectivity of mediotemporal, left temporal, and other neocortical regions. CONCLUSION: Feature selection was profoundly driven by statistical independence. Some features showed no between-group differences, or showed a trend in either direction. This indicates that clinically relevant brain alterations typical of MCI might be subtle and not inferable from group analysis.

Quantifying Pelvic Periprosthetic Bone Remodeling Using Dual-Energy X-Ray Absorptiometry Region-Free Analysis.

The gold standard tool for measuring periprosthetic bone mineral density (BMD) is dual-energy X-ray absorptiometry (DXA). However, resolution of the method is limited due to the aggregation of pixel data into large regions of interest for clinical and statistical analysis. We have previously validated a region-free analysis method (DXA-RFA) for quantitating BMD change at the pixel level around femoral prostheses. Here, we applied the DXA-RFA method to the pelvis, and quantitated its precision in this setting using repeated DXA scans taken on the same day after repositioning in 29 patients after total hip arthroplasty. Scans were semiautomatically segmented using edge detection, intensity thresholding, and morphologic operations, and elastically registered to a common template generated through generalized Procrustes analysis. Pixel-wise BMD precision between repeated scans was expressed as a coefficient of variation %. Longitudinal BMD change was assessed in an independent group of 24 patients followed up for 260 wk. DXA-RFA spatial resolution of 0.31 mm2 provided approximately 12,500 data points per scan. The median data-point precision was 17.8% (interquartile range 14.3%-22.7%). The anatomic distribution of the precision errors showed poorer precision at the bone borders and superior precision to the obturator foramen. Evaluation of longitudinal BMD showed focal BMD change at 260 wk of -26.8% adjacent to the prosthesis-bone interface (1% of bone map area). In contrast, BMD change of +39.0% was observed at the outer aspect of the ischium (3% of bone map area). Pelvic DXA-RFA is less precise than conventional DXA analysis. However, it is sensitive for detecting local BMD change events in groups of patients, and provides a novel tool for quantitating local bone mass after joint replacement. Using this method, we were able to resolve BMD change over small areas adjacent to the implant-bone interface and in the ischial region over 260 wk after total hip arthroplasty.

Magnetic resonance elastography of the brain: An in silico study to determine the influence of cranial anatomy.

PURPOSE: Magnetic resonance elastography (MRE) of the brain has demonstrated potential as a biomarker of neurodegenerative disease such as dementia but requires further evaluation. Cranial anatomical features such as the falx cerebri and tentorium cerebelli membranes may influence MRE measurements through wave reflection and interference and tissue heterogeneity at their boundaries. We sought to determine the influence of these effects via simulation. METHODS: MRE-associated mechanical stimulation of the brain was simulated using steady state harmonic finite element analysis. Simulations of geometrical models and anthropomorphic brain models derived from anatomical MRI data of healthy individuals were compared. Constitutive parameters were taken from MRE measurements for healthy brain. Viscoelastic moduli were reconstructed from the simulated displacement fields and compared with ground truth. RESULTS: Interference patterns from reflections and heterogeneity resulted in artifacts in the reconstructions of viscoelastic moduli. Artifacts typically occurred in the vicinity of boundaries between different tissues within the cranium, with a magnitude of 10%-20%. CONCLUSION: Given that MRE studies for neurodegenerative disease have reported only marginal variations in brain elasticity between controls and patients (e.g., 7% for Alzheimer's disease), the predicted errors are a potential confound to the development of MRE as a biomarker of dementia and other neurodegenerative diseases. Magn Reson Med 76:645-662, 2016. © 2015 Wiley Periodicals, Inc.

A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging.

Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.

Utility of Real Time 3D Echocardiography for the Assessment of Left Ventricular Mass in Patients with Hypertrophic Cardiomyopathy: Comparison with Cardiac Magnetic Resonance.

INTRODUCTION AND OBJECTIVES: Patients with hypertrophic cardiomyopathy (HCM) have irregular ventricular shapes with small and sometimes obliterated cavities at end-systole that affect the quantification of left ventricular mass (LVM) by conventional methods, such as M-mode or two-dimensional echocardiography. The goal of this study was to validate the use of real time three-dimensional echocardiography (RT3DE) to quantify LVM using cardiac magnetic resonance imaging (CMR) as a reference, in a large population of patients with different types of HCM. METHODS: Forty-eight consecutive patients with HCM had a complete transthoracic examination and CMR performed within 7 days. LVM was calculated by M-mode and RT3DE and compared to CMR that served as gold standard. RESULTS: Left ventricular mass calculated by RT3DE was 195 ± 41 g and 187 ± 49 g by CMR. The correlation between the two methods was moderate, with a Lin index of 0.63 and good linear correlation (r = 0.63, P < 0.0001). The correlation was high when RT3DE was of high or adequate image quality. The correlation between LVM by M-mode and CMR was poor. CONCLUSION: Three-dimensional echocardiography is an accurate method for the quantification of LVM in patients with different subtypes of HCM that is in better agreement with CMR reference values than M-mode measurements.

Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images.

PURPOSE: The primary goal of this article is to achieve an automatic and objective method to compute the Pfirrmann's degeneration grade of intervertebral discs (IVD) from MRI. This grading system is used in the diagnosis and management of patients with low back pain (LBP). In addition, biomechanical models, which are employed to assess the treatment on patients with LBP, require this grading value to compute proper material properties. MATERIALS AND METHODS: T2-weighted MR images of 48 patients were employed in this work. The 240 lumbar IVDs were divided into a training set (140) and a testing set (100). Three experts manually classified the whole set of IVDs using the Pfirrmann's grading system and the ground truth was selected as the most voted value among them. The developed method employs active contour models to delineate the boundaries of the IVD. Subsequently, the classification is achieved using a trained Neural Network (NN) with eight designed features that contain shape and intensity information of the IVDs. RESULTS: The classification method was evaluated using the testing set, resulting in a mean specificity (95.5 %) and sensitivity (87.3 %) comparable to those of every expert with respect to the ground truth. CONCLUSIONS: Our results show that the automatic method and humans perform equally well in terms of the classification accuracy. However, human annotations have inherent inter- and intra-observer variabilities, which lead to inconsistent assessments. In contrast, the proposed automatic method is objective, being only dependent on the input MRI.

Vascular dysfunction in the pathogenesis of Alzheimer's disease--A review of endothelium-mediated mechanisms and ensuing vicious circles.

Late-onset dementia is a major health concern in the ageing population. Alzheimer's disease (AD) accounts for the largest proportion (65-70%) of dementia cases in the older population. Despite considerable research effort, the pathogenesis of late-onset AD remains unclear. Substantial evidence suggests that the neurodegenerative process is initiated by chronic cerebral hypoperfusion (CCH) caused by ageing and cardiovascular conditions. CCH causes reduced oxygen, glucose and other nutrient supply to the brain, with direct damage not only to the parenchymal cells, but also to the blood-brain barrier (BBB), a key mediator of cerebral homeostasis. BBB dysfunction mediates the indirect neurotoxic effects of CCH by promoting oxidative stress, inflammation, paracellular permeability, and dysregulation of nitric oxide, a key regulator of regional blood flow. As such, BBB dysfunction mediates a vicious circle in which cerebral perfusion is reduced further and the neurodegenerative process is accelerated. Endothelial interaction with pericytes and astrocytes could also play a role in the process. Reciprocal interactions between vascular dysfunction and neurodegeneration could further contribute to the development of the disease. A comprehensive overview of the complex scenario of interacting endothelium-mediated processes is currently lacking, and could prospectively contribute to the identification of adequate therapeutic interventions. This study reviews the current literature of in vitro and ex vivo studies on endothelium-mediated mechanisms underlying vascular dysfunction in AD pathogenesis, with the aim of presenting a comprehensive overview of the complex network of causative relationships. Particular emphasis is given to vicious circles which can accelerate the process of neurovascular degeneration.

High-spatial-resolution bone densitometry with dual-energy X-ray absorptiometric region-free analysis.

PURPOSE: To outline the conceptual development of dual-energy absorptiometric (DXA) region-free analysis, quantify its precision, and evaluate its application to quantify the change in longitudinal femoral periprosthetic bone mineral density (BMD) in patients during the 12 months after total hip arthroplasty. MATERIALS AND METHODS: All subjects had undergone total hip arthroplasty for idiopathic arthritis, and the scans were collected as part of previous ethically approved studies (1998-2005) for which informed consent was provided. Contemporary image processing approaches were used to develop a region of interest-free DXA analysis method with increased spatial resolution for assessment of proximal femoral BMD. The method was calibrated, and its accuracy relative to a proprietary algorithm was assessed by using a hip phantom. The precision of the method was examined by using repeat DXA acquisitions in 29 patients, and its ability to allow spatial resolution of localized periprosthetic BMD change at the hip was assessed in an independent group of 19 patients who were measured throughout a 12-month period. Differences were evaluated with t tests (P < .05). RESULTS: The method allowed spatial resolution of more than 10 000 individual BMD data points on a typical archived prosthetic hip scan. The median data point-level error of the method after calibration was -1.9% (interquartile range, -7.2% to 3.5%) relative to a proprietary algorithm. The median data point-level precision, expressed as a coefficient of variation, was 1.4% (interquartile range, 1.2%-1.6%). Evaluation of BMD change in a model of periprosthetic bone loss demonstrated large but highly focal changes in BMD that would not be resolved by using traditional region of interest-based analysis approaches. CONCLUSION: The proposed approach provides a quantitative, precise method for extracting high-spatial-resolution BMD data from existing DXA datasets without the limitations imposed by region of interest-based analysis.

Automatic cardiac LV segmentation in MRI using modified graph cuts with smoothness and interslice constraints.

PURPOSE: Magnetic resonance imaging (MRI), specifically late-enhanced MRI, is the standard clinical imaging protocol to assess cardiac viability. Segmentation of myocardial walls is a prerequisite for this assessment. Automatic and robust multisequence segmentation is required to support processing massive quantities of data. METHODS: A generic rule-based framework to automatically segment the left ventricle myocardium is presented here. We use intensity information, and include shape and interslice smoothness constraints, providing robustness to subject- and study-specific changes. Our automatic initialization considers the geometrical and appearance properties of the left ventricle, as well as interslice information. The segmentation algorithm uses a decoupled, modified graph cut approach with control points, providing a good balance between flexibility and robustness. RESULTS: The method was evaluated on late-enhanced MRI images from a 20-patient in-house database, and on cine-MRI images from a 15-patient open access database, both using as reference manually delineated contours. Segmentation agreement, measured using the Dice coefficient, was 0.81±0.05 and 0.92±0.04 for late-enhanced MRI and cine-MRI, respectively. The method was also compared favorably to a three-dimensional Active Shape Model approach. CONCLUSION: The experimental validation with two magnetic resonance sequences demonstrates increased accuracy and versatility.